Local polynomial regresssion estimators in survey sampling

By F. J. Breidt, Jean D. Opsomer. 2000

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View Paper: https://www.semanticscholar.org/paper/32afd12aa7204d0474ce5c84af8f0d62cae29d59

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In Statistics and Probability

Citations in the corpus

Horvitz--Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling (2009, background): “Following the same lines as the proof of Theorem 3 in Breidt & Opsomer (2000), we get after some algebra that, for some constant C11, d2N (s, t) ≤ C11 [ n−1 + max (k,l,k′,l′)∈D4,N |E {(IkIl − πkl) (Ik′Il′ − πk′l′)}| ] |t− s|2β. (18) Applying again a maximal inequality as in the Proof of Proposition…”
Nonparametric Model Calibration Estimation in Survey Sampling (2005, methodology): “the proof of Theorem 3 in Breidt and Opsomer (2000) .”
Model-Assisted Survey Regression Estimation with the Lasso (2017, methodology): “Other specifications of the working model lead to model-assisted estimators based on local polynomial regression (Breidt and Opsomer 2000), penalized splines (Breidt, Claeskens, and Opsomer 2005; McConville and Breidt 2013), neural networks (Montanari and Ranalli 2005b), regression splines (Goga…”
Model-assisted estimation for complex surveys using penalised splines (2005, methodology): “In Breidt and Opsomer (2000), a nonparametric model-assisted regression estimator was proposed, which allows the relationship between the variables to be any smooth function, not just one that belongs to a pre-specified parametric family.”
Selecting the amount of smoothing in nonparametric regression estimation for complex surveys (2005, methodology): “As shown in Breidt and Opsomer [5], Varp(t̂∗ y ) in equation (4) is an asymptotic approximation to MSEp(t̂y) and the MSE of t̂y , and both are consistently estimated by”
Model-Assisted Estimation of Forest Resources With Generalized Additive Models (2007, methodology): “This extends the nonparametric model-assisted methodology of Breidt and Opsomer (2000), who considered only univariate models and single-phase estimation.”
Endogenous post-stratification in surveys: Classifying with a sample-fitted model (2008, methodology): “This can be done using linear models as in the generalized regression estimation (GREG) approach (Cassel, S¨arndal and Wretman [3]), nonlinear models (Wu and Sitter [14]) or nonparametric models (Breidt and Opsomer [ 2 ] and Breidt, Claeskens and Opsomer [1]).”
Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model (2003, methodology): “Breidt and Opsomer (2000) use the local polynomial kernel as the smoothing tool and develop a design-consistent model-assisted estimator of the total.”
The Generalized Oaxaca-Blinder Estimator (2020, background): “For example, Breidt and Opsomer (2000) studied the asymptotic properties of kernel regression in survey sampling settings (where design-based inference is the norm), but used simplifying “iid” assumptions in their analysis.”
To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling (2004, background): “Robustness can still be achieved by positing regression models that make weak parametric assumptions (Breidt and Opsomer 2000; Zheng and Little 2003).”
Properties of design-based functional principal components analysis (2008, background): “Hypothesis (A5) is a technical assumption that is similar to assumption A7 in Breidt and Opsomer (2000) .”